1. Field of the Invention
The present invention relates to neural networks and, more particularly, to a Aggregate Neural Semantic Network for processing output of multiple search engines by selecting relevant search results based on user preferences from prior searches and/or multiple searches.
2. Description of the Related Art
The World Wide Web (“web”) contains a vast amount of information. Locating a desired portion of the information, however, can be challenging. This problem is compounded because the amount of information on the web and the number of new users inexperienced at web searching are growing rapidly.
Search engines typically return hyperlinks to web pages in which a user is interested. Generally, search engines base their determination of the user's interest on search terms (referred to as a search query) entered by the user. The goal of the search engine is to provide links to high quality, relevant results to the user based on the search query. Typically, the search engine accomplishes this by matching the terms in the search query to a corpus of pre-stored web pages. Web pages that contain the user's search terms are considered to be “hits” and are returned to the user. However, the hits typically contain a lot of irrelevant information.
In an attempt to increase the relevancy and quality of the web pages returned to the user, a search engine may attempt to sort the list of hits so that the most relevant or highest quality pages are at the top of the list of hits returned to the user. For example, the search engine may assign a rank or score to each hit, where the score is designed to correspond to the relevance or importance of the web page.
However, determining appropriate scores for a particular user can be a difficult task. For one thing, the importance of a web page to the user is inherently subjective and depends on the user's interests, knowledge, and attitudes. There is, however, much that can be determined objectively about the relative importance of a web page. Conventional methods of determining relevance are based on the contents of the web page. More advanced techniques determine the importance of a web page based on more than just the content of the web page.
The overriding goal of a search engine is to return the most desirable set of links for any particular search query. Keyword generation is one of the aspects of providing search results and managing the search process. Keywords identify what the documents are “about”—they may be words that are mentioned in the documents themselves, or they may be concepts that are related to the meaning of the document, and which capture, in one term or a phrase, the meaning of the document.
The same words (i.e., terms) can mean different things or concepts to different users. Typically, the same search query will return the same set of results. However, while one user may include the word “apple” in the search query looking for information on Apple™ computers, another user may be simply interested in the apple fruits.
Accordingly, there is a need in the art for an effective and efficient system and method for processing output of search engines by selecting most relevant search results based on accumulated user preferences.